Diabetes Patients Analysis Using Python

Description this dataset is originally from the National Institute of Diabetes and kidney diseases. The objective of the dataset is to dignostically predict whether or not a patient has diabetes. Based on a certain diagnostic measurement included in the dataset. Several constrains were placed on the selection of these instances from a larger dataset. In particular all patients here are females at least 21 old of pima Indian Heritage.

Content

The dataset consists of several medical predictor variables and one target variable, Outcome predictor variables includes the number of pregnancies the patients has had their BMI, Glucose level, BP, Age and so on.

Problem Statement

By using these patients records, we will try to accurately predict whether or not the patients in dataset have diabetes or not.

Data Description

Columns; Pregnancies. Glucose. Blood Perssure. SkinThickness. Insulin. Body max index (BMI). Age. Outcome. DiabetesPedigreeFunction.

Business Objective and Constant

1. The cost of mis-classification can be very very high.
2. There in no strict letacy concerns.
3. Because this is from health domain, we have to be very cautiousabout the result.

Importing Liabraries

import pandas as pd 
import numpy as np
from pandas.plotting import scatter_matrix

import matplotlib.pyplot as plt
import seaborn as sns

import warnings
warnings.filterwarnings('ignore')
%matplotlib inline

Loading Dataset

# Loading Dataset
df = pd.read_csv(r'C:\Users\hp\OneDrive\Documents\diabetes.csv')

df.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1

Exploratory Dataset Analysis

# Total number of colunms
df.columns

Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],
      dtype='object')

# Information about dataset
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Pregnancies               768 non-null    int64  
 1   Glucose                   768 non-null    int64  
 2   BloodPressure             768 non-null    int64  
 3   SkinThickness             768 non-null    int64  
 4   Insulin                   768 non-null    int64  
 5   BMI                       768 non-null    float64
 6   DiabetesPedigreeFunction  768 non-null    float64
 7   Age                       768 non-null    int64  
 8   Outcome                   768 non-null    int64  
dtypes: float64(2), int64(7)
memory usage: 54.1 KB

# more to know about dataset
df.describe()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
count	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000
mean	3.845052	120.894531	69.105469	20.536458	79.799479	31.992578	0.471876	33.240885	0.348958
std	3.369578	31.972618	19.355807	15.952218	115.244002	7.884160	0.331329	11.760232	0.476951
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.078000	21.000000	0.000000
25%	1.000000	99.000000	62.000000	0.000000	0.000000	27.300000	0.243750	24.000000	0.000000
50%	3.000000	117.000000	72.000000	23.000000	30.500000	32.000000	0.372500	29.000000	0.000000
75%	6.000000	140.250000	80.000000	32.000000	127.250000	36.600000	0.626250	41.000000	1.000000
max	17.000000	199.000000	122.000000	99.000000	846.000000	67.100000	2.420000	81.000000	1.000000

# Dataset with Transpose
df.describe().T

	count	mean	std	min	25%	50%	75%	max
Pregnancies	768.0	3.845052	3.369578	0.000	1.00000	3.0000	6.00000	17.00
Glucose	768.0	120.894531	31.972618	0.000	99.00000	117.0000	140.25000	199.00
BloodPressure	768.0	69.105469	19.355807	0.000	62.00000	72.0000	80.00000	122.00
SkinThickness	768.0	20.536458	15.952218	0.000	0.00000	23.0000	32.00000	99.00
Insulin	768.0	79.799479	115.244002	0.000	0.00000	30.5000	127.25000	846.00
BMI	768.0	31.992578	7.884160	0.000	27.30000	32.0000	36.60000	67.10
DiabetesPedigreeFunction	768.0	0.471876	0.331329	0.078	0.24375	0.3725	0.62625	2.42
Age	768.0	33.240885	11.760232	21.000	24.00000	29.0000	41.00000	81.00
Outcome	768.0	0.348958	0.476951	0.000	0.00000	0.0000	1.00000	1.00

Data cleaning

# checking for null values or Not
df.isnull()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	False	False	False	False	False	False	False	False	False
1	False	False	False	False	False	False	False	False	False
2	False	False	False	False	False	False	False	False	False
3	False	False	False	False	False	False	False	False	False
4	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...
763	False	False	False	False	False	False	False	False	False
764	False	False	False	False	False	False	False	False	False
765	False	False	False	False	False	False	False	False	False
766	False	False	False	False	False	False	False	False	False
767	False	False	False	False	False	False	False	False	False

768 rows × 9 columns

# checking for total null values
df.isnull().sum()

Pregnancies                 0
Glucose                     0
BloodPressure               0
SkinThickness               0
Insulin                     0
BMI                         0
DiabetesPedigreeFunction    0
Age                         0
Outcome                     0
dtype: int64

On these columns the value 0 does not make any sense and thus indicate missing value. Following columns have invalid zero value; Glucose, Bloodpressure, SkinThickness,Insulin,BMI. it is better to replace zeros with NAN since after that counting them would be easierand zeros need to be replaced.

df_copy = df.copy
df_copy=df_copy(deep= True)
df_copy[['Glucose', 'BloodPressure','SkinThickness','Insulin','BMI']]=df_copy[['Glucose', 'BloodPressure', 'SkinThickness','Insulin', 'BMI']]

print(df_copy.isnull().sum())

Pregnancies                 0
Glucose                     0
BloodPressure               0
SkinThickness               0
Insulin                     0
BMI                         0
DiabetesPedigreeFunction    0
Age                         0
Outcome                     0
dtype: int64

Data Visualization

# Plotting the data distribution plots
df.hist(figsize=(17,14))
plt.show()

# inputing NAN values for the columnsin accordance with their distribution
df_copy['Glucose'].fillna(df_copy['Glucose'].mean(), inplace= True)
df_copy['BloodPressure'].fillna(df_copy['BloodPressure'].mean(), inplace= True)
df_copy['SkinThickness'].fillna(df_copy['SkinThickness'].median(), inplace= True)
df_copy['Insulin'].fillna(df_copy['Insulin'].median(), inplace= True)
df_copy['BMI'].fillna(df_copy['BMI'].median(), inplace= True)

# plotting the distribution after removing the NAN values
p= df_copy.hist(figsize=(20,20))

# plotting the Outcome
df['Outcome'].value_counts().plot(kind="bar")

<Axes: xlabel='Outcome'>

The above graph shows that the data is based towards datapoints having outcome value as 0 where it means that diabetes was not present actually. The number of non diabetic is almost twice the number of diabetic patients.

# plot scatter matrix of uncleaned data

p= scatter_matrix(df, figsize=(20,20))

# plotting pair plots for the data
sns.pairplot(df_copy, hue='Outcome')
plt.show()

High Glucose Level in Pregnancy more likely. A contribution of high glucose level and a BMI above 30 boosts the risk of diabetes.

Correlation all the features

# creating a heatmap
plt.figure(figsize=(12,10))
p=sns.heatmap(df.corr(), annot = True, cmap ="Reds")

fig, ax = plt.subplots(1,2, figsize=(20,7))

sns.countplot(df, x = "Outcome", ax = ax[0])
df["Outcome"].value_counts().plot.pie(explode= [0.1,0], autopct= "%1.1f%%", labels= ["NO", "YES"], shadow= True, ax=ax[1])

plt.show()

We obeserve from the above plot that:

65.1% patient in this dataset do not have diabetes.

34.9% patients in the dataset has diabetes.

Conclusion

1. It has a descent level of precision, indicating that when it predicts positive cases(diabetic).
It's correct about 65% of the time.

2. Out of the 768 patients, 268 have been diagnosed with diabetes.

3. Patients with high blood pressure has greater chances of diabetes.

4. An increase in Blood Pressure BMI and skin thickness also increases.

5. Increasing level of glucose and insulin increases chances of diabetes.